Field of the Invention
The present invention concerns a method and an apparatus for implementing motion correction in magnetic resonance imaging. The invention more specifically concerns implementing such motion correction using volume navigators.
Description of the Prior Art
Navigator-based prospective motion correction methods are known that compensate patient motion during the acquisition of magnetic resonance (MR) image data. Unlike retrospective methods, in such prospective methods motion is detected and compensated during the measurement (image data acquisition) in real-time. The detection of the patient motion can be achieved by the use of navigators, which are activated during dead times of the image acquisition sequence that is used to operate the MR scanner.
An example of a known prospective motion-correction method is described in the article by Tisdall et al. entitled “MPRAGE Using EPI Navigators For Prospective Motion Correction,” Proceedings of the 17th Annual Meeting of International Society of Magnetic Resonance in Medicine 2009. In this procedure, low resolution 3D EPI navigator volumes are acquired with a base resolution of 32×32×32. The data for such navigator volumes can be acquired within 500 ms. Each data acquisition sequence that has a sufficiently long dead time can be equipped with such navigators, in order to support real-time motion compensation.
In practice, navigator images are reconstructed for navigator data acquired at a point in time t=t1, and an image based, six-degree-of-freedom rigid-body registration to a reference volume is then done. Typically, the reference volume is acquired before the acquisition of the motion-corrected sequence begins (at a point in time t=0). The detected motion parameters are fed back to the control computer that formulates the sequence for operating the MR scanner, and the control computer automatically adapts the imaging field-of-view (FOV) to compensate for the detected motion at time t1. Motion which may occur between t1 and the partial acquisition of the image data in the motion-compensated sequence is not considered. As a result, the actual motion-compensation lags slightly behind the data acquisition, and cannot correct for all motion. Slowly occurring motion drifts, however, can be compensated very well.
To enable real-time motion correction, the motion detection module of the processor must be able to provide motion estimates very quickly. Therefore, a rigid body model assumption with six-degrees-of-freedom (three translational and three rotational) is sometimes generated. For MR data acquisitions from the head of a subject, this model assumption is reasonable. The aforementioned task of registering low resolution EPI image data matches the requirements for motional correction in functional magnetic resonance imaging (fMRI). Methods of this type have been proposed for high performance rigid-body motion detection and prospective correction of fMRI data, as described in Thesen et al. “Prospective Acquisition Correction for Head Motion with Image-Based Tracking for Real-Time fMRI,” Magnetic Resonance in Medicine, Volume 44, Number 3 (2000) pages 457-465 and in the inaugural dissertation of Stefan Thesen at Ruprecht-Karls-Universitat Heidelberg entitled “Retrospektive und prospektive Verfahren zur bildbasierten Korrektur von Patientenkopfbewegungen bei neurofunktioneller Magnetresonanztomographie in Echtzeit,” (“Retrospective and Prospective Methods for Image-Based Correction of Patient Head Motions in Neuro-Functional Magnetic Resonance Tomography in Real-Time”).
The same strategy described above, but without the use of additional dedicated navigator volumes, forms the basis of the commercially available operating sequence ep2d_pace (Siemens Healthcare). Instead of an additional navigator volume, the entire ep2d volume is considered for motion detection. The volume at time t=ti is reconstructed and brought into registration with the reference obtained at t=t0 during the acquisition at time t=ti+1, and is compensated at time t=ti+2. Therefore, the motion compensation lags by at least one repetition (TR) of the sequence during the acquisition of image data.
Conventional 3D image-based navigator methods make use of navigator volumes (i.e., the volume of the subject from which the navigator data are acquired) that are congruent with the imaging volume of the motion-compensated sequence in which the navigator signals are used. This has the advantage that the detected motion parameters can be sent directly back to the computer that is controlling the operation of the MR scanner to execute the imaging sequence, in order to implement motion compensation in the sequence.
In the article by Shankaranarayanan et al. entitled “Motion insensitive 3D imaging using a novel real-time image-based 3D PROspective MOtion correction method (3D PROMO),” ISMRM 15th Scientific Meeting (2007), a navigator based solution is described called 3D promo, which is also based on prospective rigid-body motion. This article notes that there are problematic regions that do not satisfy the model assumption of rigid-body motion. In the brain, these regions are primarily located in the neck, jaw, and next to the nasal cavities and sinuses. In order to improve the motion estimation and to minimize the influence of non-linear effects, the authors of this article propose to apply an extended Kalman filter to the navigator data.
An overview of prospective motion correction strategies can be found in the article by Maclaren et al. entitled “Prospective motion correction in brain imaging: a review,” Magnetic Resonance in Medicine, Volume 69, Number 3 (2013), pages 621-636.